The present invention is related to the following applications: Ser. No. 11/881,608, titled “Fleet Anomaly Detection Method”, filed on Jul. 27, 2007, and application Ser. No. 11/881,612, titled “Anomaly Aggregation Method”, filed on Jul. 27, 2007.
The system described herein relates generally to finding patterns in temporal data. More specifically, the system relates to the prediction of turbomachinery failure events by using statistical techniques and a genetic algorithm, to aggregate, identify and pattern outlying (i.e., anomalous) engineering or operational data when compared to small sets of related engineering or operational data.
In the operation and maintenance of power generation equipment (e.g., turbines, compressors, generators, etc.), sensor readings corresponding to various attributes of the machine are received and stored. These sensor readings are often called “tags”, and there are many types of tags (e.g., vibration tags, efficiency tags, temperature tags, pressure tags, etc.).
Close monitoring of these tags across time has many benefits in understanding machine deterioration characteristics (e.g., internal damage to units, compressor events, planned vs. unplanned trips). For example, increasing values (over time) of rotor vibration in a compressor, may be an indication of a serious problem. Better knowledge of deterioration in machines also improves fault diagnostic capability via a set of built-in rules or alerts that act as leading indicators for machine events. Simultaneous display of all tag anomalies together with the designed rules-alerts makes machine monitoring and diagnostics, as well as, new rule/alert creation, extremely efficient and effective. Individuals responsible for monitoring and diagnostics can have their immediate attention directed to critical deviations.
However, there is a considerable amount of noise in sensor data. To remove noise and make observations comparable across time or across machines, many different corrections need to be made and many different controlling factors need to be used. Even then, it is still very hard to simultaneously monitor many tags (there can be several hundred to thousands of tags) and diagnose the anomalies in the data.
Removing the noise from data and catching or identifying anomalies in a usable format (e.g., magnitude and direction) and then using that anomaly information in rule or model building is a needed process in many different businesses, technologies and fields. In engineering applications, monitoring and diagnostic teams typically address the problem in routine and ad-hoc fashion via control charts, histograms, and scatter plots. However, this approach necessitates a subjective assessment as to whether a given tag is anomalously high or low.
There are known statistical techniques including z-scores to evaluate the degree to which a particular value in a group is an outlier, that is, anomalous. Typical z-scores are based upon a calculation of the mean and the standard deviation of a group. While a z-score can be effective in evaluating the degree to which a single observation is anomalous in a well populated group, z-scores have been shown to lose their effectiveness as an indication of anomalousness when used on sets of data that contain only a small number of values.
When calculating anomaly scores, it is often the case that there are only a few values with which to work. For instance, when comparing a machine (e.g., a turbine) to a set of peer machines (e.g., similar turbines), it is often the case that it is difficult to identify more than a handful of machines that can legitimately be considered peers of the target machine. In addition, it is often desirable to evaluate the performance of machines that may only have been in operation under the current configuration for a limited period of time. As a result, it is often not desirable or accurate to use standard z-scores as a measurement for anomaly scores since standard z-scores are not robust with small datasets.
Accordingly, a need exists in the art for a system that can predict failure events, before they occur, in machines by analyzing past and/or current operational data.